# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Various positional encodings for the transformer.
"""
import math
from typing import List

import torch
from torch import nn


class Joiner(nn.Sequential):
    """
    Joins the positional embeddings with the backbone output
    """

    def __init__(self, backbone, position_embedding):
        super().__init__(backbone, position_embedding)

    def forward(self, x: torch.Tensor):
        """
        Forward pass of the sequential model
        self[0]: backbone
        self[1] position embedding
        """

        
        """old forward, x was called tensor here: 
        xs = self[0](x1)
        out: List[torch.tensor()] = []
        pos = []
        for x in xs:
            out.append(x)
            # position encoding
            pos.append(self[1](x).to(x.tensors.dtype))
        return out, pos 
        """
        
        backbone_out = self[0](x)
        positional_out = self[1](backbone_out)
        return backbone_out, positional_out


class PositionEmbeddingSine(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """
    def __init__(self, num_pos_feats=64, temperature=10000,
                 normalize=False, scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, tensor_list: torch.Tensor):
        #x = tensor_list.tensors
        x = tensor_list
        #mask = tensor_list.mask
        #assert mask is not None
        #not_mask = ~mask
        #mask (bs, seqlength, ) one dimensional
        #we do with batch size
        not_mask = torch.ones((x.size()[0], x.size()[2]), device=x.device)
        x_embed = not_mask.cumsum(1, dtype=torch.float32)

        if self.normalize:
            eps = 1e-6
            x_embed = x_embed / (x_embed[:, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats,
                             dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
        #unsqueeze
        pos_x = x_embed[:, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :,  0::2].sin(),
                             pos_x[:, :, 1::2].cos()), dim=3).flatten(2)

        pos = pos_x.permute(0, 2, 1) # batch, hidden dim, timestamps
        #pos = torch.cat((pos_x, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos


class PositionEmbeddingLearned(nn.Module):
    """
    Absolute pos embedding, learned.
    """
    def __init__(self, num_pos_feats=256):
        super().__init__()
        self.row_embed = nn.Embedding(50, num_pos_feats)
        self.col_embed = nn.Embedding(50, num_pos_feats)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.uniform_(self.row_embed.weight)
        nn.init.uniform_(self.col_embed.weight)

    def forward(self, tensor_list: torch.Tensor):
        x = tensor_list.tensors
        h, w = x.shape[-2:]
        i = torch.arange(w, device=x.device)
        j = torch.arange(h, device=x.device)
        x_emb = self.col_embed(i)
        y_emb = self.row_embed(j)
        pos = torch.cat([
            x_emb.unsqueeze(0).repeat(h, 1, 1),
            y_emb.unsqueeze(1).repeat(1, w, 1),
        ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
        return pos


def build_position_encoding(hidden_dim, pos_embedding='sine'):
    N_steps = hidden_dim
    if pos_embedding in ('v2', 'sine'):
        # TODO find a better way of exposing other arguments
        position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
    elif pos_embedding in ('v3', 'learned'):
        position_embedding = PositionEmbeddingLearned(N_steps)
    else:
        raise ValueError(f"not supported {pos_embedding}")
    return position_embedding
